Large Margin Metric Learning
Large Margin Metric Learning is a metric learning framework that belongs to the class of supervised distance metric learning methods, distinguished by its incorporation of a large-margin criterion to maximize the separation between inter-class distances while minimizing intra-class distances in the learned embedding space.
122
Publications
17.1K
Citations
386
Authors
162
Institutions
Publications per year
2017–2026
57
Variants
Lmml
Parents
122
Additive Margin Softmax for Face Verification
IEEE Signal Processing Letters · 2018 · 1.1K citations · Full text
Learning Deep Structure-Preserving Image-Text Embeddings
Liwei Wang, Yin Li, Svetlana Lazebnik · 2016 · 808 citations
1–5 of 122
386
6
h-index
4
h-index
4
h-index
3
h-index
3
h-index
1–5 of 386
162
Nanyang Technological University
Singapore, Singapore
9 h-index
23 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
7 h-index
14 publications
702 citations
Tsinghua University
Beijing, China
5 h-index
13 publications
844 citations
University of Technology Sydney
Sydney, Australia
5 h-index
10 publications
567 citations
Georgia Institute of Technology
Atlanta, United States
5 h-index
7 publications
2K citations

Polytechnique Montréal
Montreal, Canada
3 h-index
3 publications
98 citations

Northwestern Polytechnical University
Xi'an, China
3 h-index
10 publications
345 citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
11 publications
598 citations
1–8 of 162
Repository · 12 publications · 3.3K citations
11
h-index
Conference · 8 publications · 335 citations
8
h-index
5 publications · 145 citations
5
h-index
4 publications · 195 citations
4
h-index
3 publications · 105 citations
3
h-index
1–5